Driven by the success of general object detection, many pedestrian detectors follow the anchor-based/anchor-free paradigm. Specifically, the anchor-based detectors are proposed in the two-stage/one-stage framework.
RPN+BF [1]} discusses the issue that improves performance of Faster R-CNN for pedestrian detection with a RPN followed by shared boosted forests.
MS-CNN [2]} attempts to introduce various receptive fields that match different object scales to tackle with multi-scale problem in pedestrian detection.
Recently, methods such as PBM [3]} use both part and full-body features to more accurately classify and localize pedestrians especially in crowded scenes.
However, aforementioned detectors focus more on feature representation, and less attention is paid to the inherent drawbacks of IoU-based assignment method during the training procedure.
